Prior art in computers consists of analog and digital processors. The analog devices implement algebra and calculus functions for the purpose of solving systems of equations while digital devices implement arithmetical and logical statements in a serial fashion and do not use parallel processing.
The pulse-coupled neural network (PCNN), derived from the Eckhorn mathematical linking field model (see "Feature Linking via Synchronization among Distributed Assemblies: Simulations of Results from Cat Visual Cortex," by R. Eckhorn, H. J. Reitboeck, M. Arndt and P. Dicke, Neural Computation 2, pp. 293-307 (1990)), is a higher order neural network which uses pulses and pulse coupling to form linking waves and time signals. It presents a general method of combining information, transmitting information to and from processing sites as time signals. The time signals encode in their pulse phase structure the geometrical content of the input spatial distributions and other feature information. It combines, or fuses, the information from separate sources by multiplying their time signals to form a composite time signal. The PCNN allows time-synchronous pulse activity as well as parallel processing through neural network algorithms. It contains both sums and products that can be combined to make logical rules for information fusion.
The pulse-coupled neural network model neuron has three parts: (1) a pulse generator (2) a linking mechanism and (3) receptive field structures. The network itself, as distinguished from the individual neuron, is composed of many such neurons all interconnected with each other and further connected to external signal sources and external systems. With reference to FIG. 1 which is a diagram of a model neuron, each part of the neuron is explained below in detail. In FIG. 1, as in the other figures, like numbers represent like parts and arrows denote the direction of signal travel.
(1) The Pulse Generator:
As shown, pulse generator 101 consists of threshold discriminator 103 followed by pulse former 105. The discriminator is triggered when an input signal U.sub.j, called the internal activity, exceeds the value of the threshold signal .theta..sub.j at some point in time. When such an event occurs, the discriminator sends a signal to the pulse former causing the latter to produce spike-like pulse trains. These pulses are produced at a fixed rate and are all identical. The pulse former output is the neuron's output, Y.sub.j, which feeds to other neurons and which also feeds back to threshold element 107. The threshold element is a leaky integrator, modeled by a first-order relaxation or exponential decay process. It is recharged by the pulses produced by the pulse former until it is driven above the level of the internal activity at which time the pulse generator stops making pulses.
(2) The Linking Mechanism:
Linking mechanism 117 allows an input signal, called the modulating linking input 1+.beta..sub.j L.sub.j, to modulate the standard input, called the feeding input F.sub.j, to pulse generator 101. The modulating linking input is produced by adding, at summing junction 113, a bias offset to L.sub.j which is derived from spatial and/or temporal groups of pulses from other neurons and algebraically multiplying the result at multiplier 111 by the feeding input F.sub.j that comes from a standard video camera. The resulting product U.sub.j is then used as the modified input to the pulse generator. The modulating linking input which travels by channel 109 is preprocessed prior to the biasing and multiplication that happen at summing junction 113 and multiplier 111, respectively.
(3) Receptive Field Structures:
The aforementioned pre-processing is done by receptive field structures 115 of the model neuron. The temporal preprocessing usually consists of convolving the input pulses Y(Kl) through Y(Kn) from other neurons (first to nth neurons) with exponential decay time kernels 119 through 121. These are first-order relaxation processes and are identical to that used in threshold element 107 except that the characteristic decay time of time kernels 119 through 121 is shorter than that of threshold element 107. Subsequent spatial pre-processing consists of forming a weighted sum, at summing junction 113 (which also adds the linking strength .beta.), of pulses over a given spatial region. This weighted sum is the linking input .beta..sub.j L.sub.j.
As illustrated in FIG. 1, the receptive field structures consist of means for receiving the pulses from other model neurons and a source of video image and combining them into appropriate input signals for the linking mechanism. Each model neuron can have many receptive fields, both feeding and linking, and the several receptive fields can each receive a pulse from the same transmitting neuron at the same time. The receptive field structures receive many inputs and generates only one output, U.sub.j. A receptive field can have a single or multiple connection points each of which is available for connection with one input pulse signal. A multiplicative weight (not illustrated) can be applied to the amplitude of the input pulse and the value of the weight can be different for every connection point. The connection point can further have a first-order relaxation process which acts on the weighted input pulse and produces an exponentially-weighted time average. All of the first-order relaxation processes can have different and distinct decay time constants and all of the receptive field connection points can have different time decay constants. Following the connection point weighting and the first-order decay processes, the signals are linearly summed at summing junction 113 and biased to become modulating linking input, 1+.beta..sub.j L.sub.j.